The age-old adage “Customer is King” has never been as prevalent as it is in today’s competitive market landscape. Especially, within the media and entertainment industry, the space is overrun by a growing number of platforms, streaming services and entertainment genres. Deciding what to invest into and what to promote has become a struggle for most if not all media companies worldwide. Especially within an industry worth over USD $2 trillion (Selerity) as of 2020, how do they streamline in order to make better and more effective decision making? The answer is simple. Understanding Big Data.
Today’s digital era has made information sharing abundant. Both voluntary and involuntary information and movements are tracked. With the right tools and processing abilities at an organization’s disposal, the large scale data transmitted back and forth from customers to the platforms they engage with, even at a micro-scale, can be streamlined to produce meaningful information. This information, in turn, can be used to narrow down promotion opportunities, production ventures and even the colors or typeface used on the platform.
Data analytics has changed the way businesses interact with their customers. Especially in an industry-driven aggressively by customer demands, the media and entertainment industry has completely altered their approach, factoring in data analytics to great success.
What is Data Analytics?
Data analytics is a methodology employed to streamline large data sets down to meaningful information. The process consists of tools and techniques that handle data manipulation and analysis while simultaneously tackling storage, collection and organization of the same. The objective of data analytics endeavors is to ensure a pure statistical analysis is conducted on actual information to detect patterns and trends that appear meaningful to an organization. This information can be critical to editing ongoing business strategies and planning better for the future.
There are four key types of data analytics activities, these are;
- Descriptive Analytics: Descriptive analytics offers a business the ability to understand the past and present. Historical and current data is procured from multiple verifiable sources in order to accurately deduce existing market trends and patterns. This is considered a form of developing business intelligence.
- Diagnostic Analytics: Diagnostic analytics offer insight into why a certain activity or trend is happening. This method of data analytics capitalizes on the information procured through descriptive analytics in order to understand why previous performance occurred as it did.
- Predictive Analytics: Predictive analytics help decipher what is likely to happen in the future. This method employs a wide array of techniques, including statistical modelling, forecasting and the use of ML with diagnostic and descriptive analytics to make strong predictions about possible future outcomes. This kind of analytics relies heavily on deep learning and machine learning to churn out the most accurate results.
- Prescriptive Analytics: A “What Do We Need To Do?” approach to analytics; prescriptive analytics is a more advanced method of data analytics. The process involves testing and the use of specific techniques employed on a case by case basis to understand what actions are likely to churn out the desired outcome.
Data analytics also consists of a number of techniques and tools used to understand large scale information and turn it into meaningful data. There are seven popular methods; Regression Analysis, Monte Carlo simulations, Factor Analysis, Cohort Analysis, Cluster Analysis, Time Series Analysis and Sentiment Analysis. These methods work to understand data trends and patterns that can, in turn be used to make better decisions surrounding business operations.
Data Disruption and The Media
Today’s media and entertainment industry is saturated with technology and media devices. As a result, media businesses are able to connect with their consumers at multiple levels and craft more meaningful and engaging experiences that can be accessed on the go. Technology has allowed the redefinition of celebrities and fame to account for social media stars and reality television. With the constantly evolving definition of what entertainment means today, entertainment businesses have begun leveraging big data and data analytics to move processes along a meaningful trajectory.
Big data harnessing has changed multiple key parameters within the entertainment industry, including;
1. Understanding Customer Preferences and Needs
It is predicted by the end of this year, over 209 million (Selerity) people will have paid for and would be using video-on-demand services such as Netflix, Hulu and Amazon Prime. Millennials have migrated from scheduled broadcasting to accessing content as and when they choose to. In order to keep their content relevant to their audience, a large number of streaming platforms employ data analytics in more real-time application. Using data analytics helps these platforms understand what the pattern of viewing is for each customer and offers inbuilt solutions to offer recommendations and take options that are likely unwanted out of view for the customer.
Maximizing the options a customer wants to view is more likely to keep them on your platform as opposed to services that offer large selections of ever-rotating content.
2. Expand Potential Revenue Generation
Big data analytics, when used correctly, can have a major positive impact on revenue generation. Big data helps organizations understand how and where to correctly incentivize consumer behaviour in order to understand the market in actuals. This works particularly well with targeted advertising. Businesses are able to understand consumption patterns to churn out advertisements within services such as YouTube and Instagram that are likely to resonate with the customer. With YouTube video consumption growing by 100% (Medium) annually, understanding where and how to place advertising can make a significant contribution towards building further revenue. Disintegrating demographic information and understanding where consumers spend their time helps create patterns that can be used to share only meaningful information with a wide array of consumers.
With every successfully targeted advertisement, the company hosting the advertisement is able to generate further revenue.
3. Redefine Media Streams
In the digital age, there are a number of devices and screens content can be consumed from. Using big data analytics helps entertainment companies understand when content is more likely to be consumed and what device/s the content is consumed on. Big data in particular, helps businesses take this information to as large a scale as they feel comfortable with. Entertainment companies can monitor large areas such as cities and regions and can take it down to area codes for more local-centric distribution opportunities.
One of the core benefits of incorporating big data analytics is the real-time aspect of the process. Data is harnessed as it is produced for the best decisions possible. Big data also helps businesses understand patterns with subscription or un-subscription along with possible routes to take in order to generate viable leads that can be converted into customers. Additionally, providing information such as email/social media sentiments and call records help introduce additional parameters that can be used to ascertain customer interest.
4. What does Performance Analysis mean?
While revenue generation has been a steady indicator of the success or failure of a project, customer-centric impressions and parameters must be factored in to gauge a comprehensive picture. Big data analytics offer information across all pertinent and adjacent scales allowing businesses to have a 360-degree view of the conducted activities. To translate the big data into reports, companies employ automated report writing tools often fueled by natural language generation (both forms of artificial intelligence). The end result is actionable information deduced from actuals allowing businesses to not only understand how well a project fared but also how to avoid or encourage similar responses moving forward.
Conclusion
Introducing big data or data analytics into an industry as consumer driven as media and entertainment is critical to maintaining relevance. Consumer demands are constantly changing, and keeping a viewer engaged for extended periods of time is often difficult to discern. Big data offers insights provided by micro and macro movements in real-time. This means the information relayed is not only accurate but relevant. The media & entertainment industry is able to harness this information and churn out relevant and meaningful content along with other customer-facing operations to ensure a wholesome customer experience that is likely to bring them back.
Data analytics helps keep consumer content relevant and current.
